Optimization of recycled rubber self-compacting concrete: Experimental findings and machine learning-based evaluation.

This research aims to assess the rheological and mechanical characteristics of Self-compacting concrete (SCC) incorporating waste tire rubber aggregates (WRTA) as an interim substitute for coarse aggregates. However, the standard experimental modeling approach has significant obstacles when it comes to overcoming the nonlinearity and environmental susceptibility of concrete parts. Therefore, linear regression (LR) and extreme gradient boosting (XGBoost) were used as two standard single machine learning (ML) models to predict the aforementioned rubberized SCC features. In this study, conventional coarse aggregates were supplanted with WRTA at 0%, 5%, 10%, and 20% to uncover the optimal proportion of coarse aggregates substituting rubber. To find the optimum amount of WRTA to use as a substitute, the study follows the impacts of rubber on the self-compacting rubberized concrete's (SCRC) rheological and mechanical characteristics. The consequences on fresh properties were investigated by the slump flow, J-ring, and V-funnel tests, while compressive and splitting tensile strengths tests were conducted to assess mechanical properties. Increasing WRTA test outputs indicated a deterioration in workability and hardened qualities. While a 10% swapping ratio is deemed feasible for producing SCRC, optimal results were achieved by reducing environmental impacts and efficiently managing a significant volume of rubber tire waste with a 5% substitution of rubber within the coarse aggregates. The research findings indicated a noticeable decrease in fresh properties as the WRTA content increased. Notably, after 28 days, a 10% WRTA substitution led to a 34% reduction in compressive strength and a 28% decrease in splitting tensile strength, satisfying ACI standards. Furthermore, XGBoost demonstrated superior predictive performance with the highest R2 values, outperforming the LR model and affirming its efficacy in delivering more accurate predictions.

Artificial intelligence and machine learning applications in the project lifecycle of the construction industry: A comprehensive review.

The construction industry faces many challenges, including schedule and cost overruns, productivity constraints, and workforce shortages. Compared to other sectors, it lags in digitalization in every project phase. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies revolutionizing the construction sector. However, a discernible gap persists in systematically categorizing the applications of these technologies throughout the various phases of the construction project life cycle. In response to this gap, this research aims to present a thorough assessment of the deployment of AI and ML across diverse phases in construction projects, with the ultimate goal of furnishing valuable insights for the effective integration of these intelligent systems within the construction sector. A thorough literature review was performed to identify AI and ML applications in the building sector. After scrutinizing the literature, the applications of AI and ML were presented based on a construction project life cycle. A critical review of existing literature on AI and ML applications in the building industry showed that AI and ML applications are more frequent in the planning and construction stages. Moreover, the opportunities for AI and ML applications in other stages were discussed based on the life cycle categorization and presented in this study. The practical contribution of the study lies in providing valuable insights for the effective integration of intelligent systems within the construction sector. Academically, the research contributes by conducting a thorough literature review, categorizing AI and ML applications based on the construction project life cycle, and identifying opportunities for their deployment in different stages.

Eco-friendly concrete incorporating palm oil fuel ash: Fresh and mechanical properties with machine learning prediction, and sustainability assessment.

Rising natural resource consumption leads to increased hazardous gas emissions, necessitating the concrete industry's focus on sustainable alternatives like palm oil fuel ash (POFA) to replace cement. Also, advanced machine learning (ML) techniques can uncover previously unreported insights about the effects of POFA that may be missing from the literature. Hence, this study investigates the influence of varying concentrations of POFA on fresh and mechanical characteristics with quantifying ML approaches and microstructural performance, as well as the environmental impact of structural concrete. For this, cement substitutions of 5 %, 15 %, 25 %, 35 %, and 45 % (by weight of cement) were utilized. POFA enhanced the overall concrete workability, with slump increments ranging from approximately 9 %-55 % and compacting factor increments of 4 %-12 %. Mechanical performance of POFA concrete improved up to 25 % replacement levels, with the highest enhancements observed in compressive (4.5 %), splitting tensile (36 %), and flexural (31 %) strength, for the mix containing 15 % POFA. The finer particle size of POFA improved microstructural performance by reducing porosity, aligning with the enhanced mechanical strength. The environmental impact of POFA was assessed by measuring eCO2 emissions, revealing a potential reduction of up to 44 %. Incorporating 5 %-15 % POFA yielded ideal mechanical performance results, significantly enhancing sustainability and cost-effectiveness. Regarding the ML approach, it can be observed that a low regression coefficient (R2) contrasts sharply with the higher R2 values for the random forest (RF) and the ensemble model, indicating satisfactory precision prediction with experimental results.

Exploring the synergistic effect of fly ash and jute fiber on the fresh, mechanical and non-destructive characteristics of sustainable concrete.

The utilization of waste fly ash and natural jute fiber has drawn attention to producing sustainable concrete. However, there is a lack of studies to analyze the synergistic effect of jute fiber and fly ash on the properties of concrete. Hence, the aim of this study is to investigate the combined effect of fly ash concentration and the different sizes of jute fiber on the fresh, hardened, and non-destructive testing properties of concrete. Concerning the purpose, the concrete mixes were prepared with 0.2 % and 0.4 % jute fiber incorporating 0 %, 5 %, 10 %, and 15 % replacement of cement by fly ash in concrete. For the assessment of properties, slump, density, and compacting factor test was conducted at the fresh state of concrete, whereas mechanical properties such as compressive, splitting tensile, and flexure strength test were conducted at 7 and 28 days. In addition, the non-destructive test (NDT) was also carried out to predict the destructive compressive strength by rebound hammer at 28 days. The microstructure property of optimum mix concrete was analyzed by scanning electron microscopy (SEM). It is observed that the incorporation of jute fiber decreased the slump, density, and compacting factor but increased the compressive, splitting tensile, and flexure strength, whereas the fly ash improved both fresh and hardened properties. Compared to the standard mix, fly ash-based JFRC mixtures have compressive strength improvements varying from 1.7 % to 25.9 %. The 10 % fly ash and 0.2 % volume of jute fiber exhibited a maximum of 11.64 % and 10.72 % splitting tensile and flexural strength enhancement at 28 days, respecting the control mix. In addition, the NDT strength assessment obtained 5.5 % less than destructive strength on average. From the SEM point of view, it is obtained that the 10 % fly ash with 0.2 % jute fiber composition matrix exhibits a better bonding interface and the cracking resistance nature of the concrete mix.

Integration of Rice Husk Ash as Supplementary Cementitious Material in the Production of Sustainable High-Strength Concrete.

The incorporation of waste materials generated in many industries has been actively advocated for in the construction industry, since they have the capacity to lessen the pollution on dumpsites, mitigate environmental resource consumption, and establish a sustainable environment. This research has been conducted to determine the influence of different rice husk ash (RHA) concentrations on the fresh and mechanical properties of high-strength concrete. RHA was employed to partially replace the cement at 5%, 10%, 15%, and 20% by weight. Fresh properties, such as slump, compacting factor, density, and surface absorption, were determined. In contrast, its mechanical properties, such as compressive strength, splitting tensile strength and flexural strength, were assessed after 7, 28, and 60 days. In addition, the microstructural evaluation, initial surface absorption test, = environmental impact, and cost-benefit analysis were evaluated. The results show that the incorporation of RHA reduces the workability of fresh mixes, while enhancing their compressive, splitting, and flexural strength up to 7.16%, 7.03%, and 3.82%, respectively. Moreover, incorporating 10% of RHA provides the highest compressive strength, splitting tensile, and flexural strength, with an improved initial surface absorption and microstructural evaluation and greater eco-strength efficiencies. Finally, a relatively lower CO2-eq (equivalent to kg CO2) per MPa for RHA concrete indicates the significant positive impact due to the reduced Global Warming Potential (GWP). Thus, the current findings demonstrated that RHA can be used in the concrete industry as a possible revenue source for developing sustainable concretes with high performance.